The time needed to perform animal observation increases with farm size. Automated warning systems are useful to detect pigs with an increased risk of tail-biting. These automated warning systems could work more precisely than humans and are not influenced by mood. Furthermore, continuous observations can be performed. After being warned by the system, the farmers could take a closer look at these pigs and intervene if needed. Thus, the automated system could improve economic efficiency.
These systems could assess several parameters, which are known as a precursor of tail-biting.
A tail-biting outbreak can be predicted by a change in feed intake frequency.
Wallenbeck and Keeling (2014) analysed feed intake frequency. At pen level, they found out that both the daily frequency of feeder visits and the daily feed consumption
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were lower in pigs housed in pens where tail-biting occurred than in those showing no tail-biting. Thus, sensors mounted on the trough measuring the feed consumption of a group can help the farmer to detect pens with an increased risk of tail-biting. At individual level, Wallenbeck and Keeling (2014) stated that the bitten pigs showed an increased daily frequency of feeder visits just before tail-biting and a reduced daily feed consumption during and after tail-biting. Munsterhjelm et al. (2015) observed a decrease in feed intake 20 days prior to tail-biting in bitten pigs. Thus, to measure the feed intake of every single animal provides on the one hand information on which group has an increased risk of tail-biting and, furthermore, on which pig has a higher risk of becoming a bitten pig. Feed intake at individual level could be measured by means of RFID (radio-frequency identification) chips (Reiners et al., 2009).
Not only the feeding behaviour but also the chewing activity on occupation material at pen-level increases with a higher likelihood of tail-biting (Ursinus et al., 2014).
Zonderland et al. (2003b) analysed the usage of different occupation materials, which were equipped with a sensor that measured the presence or absence of movement.
Such occupation materials can be used to detect a higher chewing activity in endangered pens.
Besides chewing activity, the general activity level within a group is higher prior to tail-biting with more pigs standing and fewer pigs sitting or lying inactively (Statham et al., 2009). This behavioural change could be detected by using movement sensors as performed in cows (Roelofs et al., 2017), or cameras. Another possibility are infrared detectors to measure the activity of pigs (Besteiro et al., 2018). Lee et al. (2016) automatically recorded head-to-head knocking and chasing. Thus, it should be possible to detect mouth-to-tail contacts. However, it could be challenging to differentiate between innocuous tail-in-mouth behaviour (Schrøder-Petersen et al., 2003) and harmful tail-biting behaviour.
In addition, tail-posture can be analysed by means of cameras. D’Eath et al. (2018) for example tested an early warning system based on 3D cameras which measured the angle between a pig’s tail and its body and detected risky tail-postures which are known as an early warning signal (Zonderland et al., 2009; Lahrmann et al., 2018a).
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Therefore, the scoring key used has to be adapted to the automated observation system and tested for the accuracy and sensitivity. For instance, the difference between moving and non-moving tails can be measured by both 2D and 3D cameras.
However, measuring the angle between the tail and the back of a pig necessitates 3D cameras. Furthermore, studies on tail-postures as an early warning sign should be carried out to clarify which tail-posture is a real precursor. Several studies have stated that the tail-posture ‘hanging’ represents a risky tail-posture (Chou et al., 2018), whereas other studies have put ‘hanging tails’ in context to a relaxed pig (Kleinbeck and McGlone, 1993). These discrepancies in literature should be eliminated to develop a warning system which is suitable for ideally all farms.
As already mentioned, intensive animal observation conduces the early detection of risk factors which could lead to tail-biting. These risk factors have to be ideally removed. If this is not possible or not successful, groups with a higher level of unrest could be detected during the daily animal observation routine. In these pens, intervention measures such as providing additional occupation material should be carried out to minimise the risk for tail-biting. However, this could be disadvantageous for economic reasons, due to the longer time needed during the daily routine. This could be prevented with an automated warning system, which can evaluate the current status within a group more frequently and possibly more precisely than the employees could do. This means that employees only have a closer look at a group after being warned by the system.
Conclusion
Tail-biting has a multifactorial genesis which often leads to inconsistent research results. This was also seen in the presented studies where the treatment group (crude fibre and long trough) seemed to have no consistent effect on tail-biting. It is likely that this effect was superimposed by unknown factors of influence. Both the studies presented, and the literature show that an important factor in minimising the risk of tail-biting, after optimising all influencing conditions, is intensive animal observation performed by well-trained employees, which facilitates timely intervention measures.
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To maintain economic efficiency, further studies should focus on automated early warning systems, which support the farmers’ animal observation. As this study has shown, posture can be used as a feasible parameter, which changes prior to tail-biting.
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Anja Honeck: Influence of crude fibre in piglets’ rations and the animal-to-feeding-place ratio on tail-biting in weaning pigs
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